Estudo sobre construção de escalas com base na Teoria da Resposta ao Item: avaliação de proficiência em conteúdos matemáticos básicos
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Data
2018-05-07
Autores
Fujii, Tânia Robaskiewicz Coneglian
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Universidade Estadual Paulista (Unesp)
Resumo
Neste trabalho realizou-se um estudo sobre construção de escalas, com base na Teoria da Resposta ao Item (TRI), resultando na construção e interpretação pedagógica de uma escala de conhecimento para medir a proficiência em conteúdos matemáticos, necessários para o acompanhamento das disciplinas de cálculo e similares dos ingressantes nos cursos da área de exatas. O modelo matemático adotado nesta pesquisa foi o logístico unidimensional de três parâmetros. A estimação dos parâmetros dos itens e das proficiências dos respondentes foi feita sob enfoque bayesiano, utilizando-se o amostrador de Gibbs, algoritmo da classe dos Métodos de Monte Carlo via Cadeia de Markov (MCMC), implementado via software OpenBUGS (Bayesian inference Using Gibbs Sampling), direcionado para análise bayesiana de modelos complexos. O software BILOG-MG também foi utilizado para comparação dos resultados. O instrumento utilizado para a medida do conhecimento consistiu em uma prova composta por trinta e seis itens de múltipla escolha, cada um com cinco alternativas, sendo somente uma a correta. Os itens foram elaborados com base em uma matriz de referência construída para este fim, dividida em três temas, sendo estes “espaço e forma”, “grandezas e medidas” e “números e operações/álgebra e funções”. Cada tema é composto por competências e cada competência descreve uma habilidade que se deseja medir. Para a construção da escala proposta, optou-se por adotar uma escala com média 250 e desvio padrão 50. Nesta escala foram selecionados níveis para serem interpretados em um intervalo de 75 a 425. Para interpretação da escala proposta, foram comparados alguns métodos de posicionamento de itens âncora nos níveis selecionados. Buscando a interpretação da escala, em toda a sua amplitude, optou-se por utilizar a análise de agrupamentos hierárquicos para segmentar a escala em grupos, ou seja, em faixas de proficiência. A escala foi dividida em cinco grupos, cada grupo caracterizado com base nos itens posicionados como âncora, a partir de suas probabilidades de resposta correta e de seus valores para o parâmetro de discriminação. Embora os resultados sejam consistentes, apontam para a necessidade de um processo contínuo de aprimoramento do banco de questões e da escala de proficiência.
In this work, a study was carried out on the construction of scales, based on the Item Response Theory (IRT), resulting in the construction and pedagogical interpretation of a scale of knowledge to measure the proficiency in mathematical contents, necessary for the follow-up of Calculus and similar subjects of the students in the courses of the Exact Sciences Area. The mathematical model adopted in this research was the three parameters one-dimensional logistic. The parameters estimation of the items and proficiencies of the respondents was done using a Bayesian approach using the Gibbs sampler, Monte Carlo Methods via Markov Chain algorithm (MCMC), implemented using OpenBUGS software (Bayesian inference Using Gibbs Sampling), directed to Bayesian analysis of complex models. The BILOG-MG software was also used to compare the results. The instrument used for the measurement of knowledge consisted of a test composed of thirty-six multiple choice items, each with five alternatives, with only one correct. The items were elaborated based on a reference matrix constructed for this purpose, divided in three themes, being these “space and form”, “quantities and measures” and “numbers and operations/ algebra and functions". Each subject is composed of competencies and each competency describes a skill that one wishes to measure. In order to construct the proposed scale, we chose to adopt a scale with a mean of 250 and standard deviation of 50. In this scale, we selected levels to be interpreted in a range of 75 to 425. For the interpretation of the proposed scale, some methods of positioning anchor items at the selected levels were compared. In order to interpret the scale in all its amplitude, it was decided to use hierarchical groupings analysis to segment the scale into groups, that is, in skill bands. The scale was divided into five groups, each group was characterized based on the items positioned as anchor, from their correct response probabilities and their values for the discrimination parameter. Although the results are consistent, they point to the need for an ongoing upgrading process of questions bank and proficiency scale.
In this work, a study was carried out on the construction of scales, based on the Item Response Theory (IRT), resulting in the construction and pedagogical interpretation of a scale of knowledge to measure the proficiency in mathematical contents, necessary for the follow-up of Calculus and similar subjects of the students in the courses of the Exact Sciences Area. The mathematical model adopted in this research was the three parameters one-dimensional logistic. The parameters estimation of the items and proficiencies of the respondents was done using a Bayesian approach using the Gibbs sampler, Monte Carlo Methods via Markov Chain algorithm (MCMC), implemented using OpenBUGS software (Bayesian inference Using Gibbs Sampling), directed to Bayesian analysis of complex models. The BILOG-MG software was also used to compare the results. The instrument used for the measurement of knowledge consisted of a test composed of thirty-six multiple choice items, each with five alternatives, with only one correct. The items were elaborated based on a reference matrix constructed for this purpose, divided in three themes, being these “space and form”, “quantities and measures” and “numbers and operations/ algebra and functions". Each subject is composed of competencies and each competency describes a skill that one wishes to measure. In order to construct the proposed scale, we chose to adopt a scale with a mean of 250 and standard deviation of 50. In this scale, we selected levels to be interpreted in a range of 75 to 425. For the interpretation of the proposed scale, some methods of positioning anchor items at the selected levels were compared. In order to interpret the scale in all its amplitude, it was decided to use hierarchical groupings analysis to segment the scale into groups, that is, in skill bands. The scale was divided into five groups, each group was characterized based on the items positioned as anchor, from their correct response probabilities and their values for the discrimination parameter. Although the results are consistent, they point to the need for an ongoing upgrading process of questions bank and proficiency scale.
Descrição
Palavras-chave
Teoria da Resposta ao Item, Proficiência em conteúdos matemáticos básicos, Inferência Bayesiana, Segmentação da escala, Item Response Theory, Proficiency in basic mathematical content, Bayesian inference, Scale segmentation